Therapy control using motion prediction based on cyclic motion model

ABSTRACT

An image-guided therapy delivery system includes a therapy generator configured to generate a therapy beam directed to a time-varying therapy locus within a therapy recipient, an imaging input configured to receive imaging information about a time-varying target locus within the therapy recipient, and a therapy controller. The therapy generator includes a therapy output configured to direct the therapy beam according to a therapy protocol. The therapy controller is configured to automatically generate a predicted target locus using information indicative of an earlier target locus extracted from the imaging information, a cyclic motion model, and a specified latency, and automatically generate an updated therapy protocol to align the time-varying therapy locus with the predicted target locus.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority fromU.S. Provisional Application No. 62/289,144, filed on Jan. 29, 2016, theentire content of which is incorporated herein by reference.

TECHNOLOGY FIELD

The disclosure relates to therapy control and, more particularly, toimage-guided therapy control systems and methods using motionprediction.

BACKGROUND

Radiation therapy or “radiotherapy” can be used to treat cancers orother ailments. Generally, ionizing radiation in the form of acollimated beam is directed from an external source toward a patient.The dose of an applied radiation therapy beam or a sequence of appliedradiation therapy beams is generally controlled such that a target locuswithin the patient, such as a tumor, receives a prescribed cumulativedose of radiation, while radiation-induced damage to healthy tissuesurrounding the target locus is to be avoided. The radiation therapybeam can include high-energy photons, electrons, or other particles suchas protons.

In one approach, a radiation therapy beam can be generated, for example,at least in part using a linear accelerator. The linear acceleratoraccelerates electrons and directs the electrons to a target, such as ametallic target, to elicit high-energy photons. The high-energy photons,generally having an energy in a mega-electron-volt (MeV) range fortherapeutic use, can then be controlled, shaped, or modulated anddirected to the target locus, such as a tumor region within the patient.A specified or selectable therapy beam energy can be used, such as fordelivering a diagnostic energy level range or a therapeutic energy levelrange. Modulation of a therapy beam can be provided by one or moreattenuators or collimators. The field size and shape of the radiationbeam can be adjusted to avoid damaging healthy tissue adjacent to thetargeted tissue by conforming the projected beam to a profile of thetargeted tissue.

In one approach, a treatment plan can be developed before radiationtherapy is delivered, such as using one or more medical imagingtechniques. In such an approach, imaging can be performed in an“offline” manner. A health care provider, such as a physician, may usethree-dimensional imaging information indicative of the patient anatomyto identify a target locus along with other regions such as organs nearthe tumor. Such imaging information can be obtained using variousimaging modalities, such as X-ray, computed tomography (CT), magneticresonance imaging (MRI), positron emission tomography (PET), orsingle-photon emission computed tomography (SPECT), etc. The health careprovider can delineate the target locus that is to receive a prescribedradiation dose using a manual technique, and the health care providercan similarly delineate nearby tissue, such as organs, at risk of damagefrom the radiation treatment. Alternatively or additionally, anautomated tool can be used to assist in identifying or delineating thetarget locus. A radiation therapy treatment plan can then be createdbased on clinical or dosimetric objectives, and constraints. Thetreatment plan can then be executed by positioning the patient anddelivering the prescribed radiation therapy to the patient. The therapytreatment plan can include dose “fractioning,” whereby a sequence ofradiation therapy deliveries are provided, with each therapy deliveryincluding a specified fraction of a total prescribed dose.

As discussed above, a radiation therapy can be guided by images thatprovide knowledge of the target locus. However, certain anatomicalregions, e.g., the lungs, are subject to quasiperiodic motion such asrespiratory motion, that is significant enough to affect the treatment.For example, respiratory motion may change the locations of some organs,such as thoracic or abdominal organs. The movement of the organ causedby the respiratory motion can lead to imaging artifacts, making theimages less effective in guiding the therapy. Therefore, furtherknowledge of the respiratory motion may be needed to plan an effectiveradiation therapy. However, unlike periodic motion, quasiperiodic motiondoes not have a fixed frequency, making it harder to predict futuremotions.

Moreover, the respiratory motion typically occurs for a relatively shorttime period. However, the process of taking an image, analyzing it, anddetermining the position of the target, can take a long time. Thatresults in a latency between acquiring the mage and compensating for therespiratory motion. Therefore, the prediction of the respiratory motionis needed to allow a prediction of the target position in real time.

The disclosed methods and systems are designed to further improve themotion prediction.

SUMMARY

In accordance with the present disclosure, there is provided animage-guided therapy delivery system including a therapy generatorconfigured to generate a therapy beam directed to a time-varying therapylocus within a therapy recipient, an imaging input configured to receiveimaging information about a time-varying target locus within the therapyrecipient, and a therapy controller. The therapy generator includes atherapy output configured to direct the therapy beam according to atherapy protocol. The therapy controller is configured to automaticallygenerate a predicted target locus using information indicative of anearlier target locus extracted from the imaging information, a cyclicmotion model, and a specified latency, and automatically generate anupdated therapy protocol to align the time-varying therapy locus withthe predicted target locus.

Also in accordance with the present disclosure, there is provided amethod of adapting a therapy protocol in response to a time-varyingtarget locus. The method includes receiving imaging information aboutthe time-varying target locus within a therapy recipient, automaticallygenerating a predicted target locus using information indicative of anearlier target locus extracted from the imaging information, a cyclicmotion model, and a specified latency, and automatically generating anupdated therapy protocol to align the time-varying therapy locus withthe predicted target locus, the therapy locus established by a therapybeam provided by a therapy generator.

Also in accordance with the present disclosure, there is provided amedical system including a medical device and a controller. The medicaldevice includes a control protocol configured to control the medicaldevice. The controller system is configured to receive a measured dataset containing values of a physiologic signal caused by an anatomicalstructure on a recipient measured at a plurality of past time points andestimate a state representation for each of the past time pointsaccording to the measured data set. The state representation reflects aninternal state of the anatomical structure. The controller is furtherconfigured to train a mapping function using historical measured valuesof the physiologic signal, predict a future state representation for afuture time point based on the estimated state representations, predicta functional representation for the future time point based on thefuture state representation and the mapping function mapping staterepresentations to functional representations, calculate a future valueof the physiologic signal according to the predicted functionalrepresentation, and update the control protocol according to the futurevalue.

Also in accordance with the present disclosure, there is provided amethod for updating a control protocol of a medical device. The methodincludes receiving a measured data set containing values of aphysiologic signal caused by an anatomical structure on a recipientmeasured at a plurality of past time points and estimating a staterepresentation for each of the past time points according to themeasured data set. The state representation reflects an internal stateof the anatomical structure. The method further includes training amapping function using historical measured values of the physiologicsignal, predicting a future state representation for a future time pointbased on the estimated state representations, predicting a functionalrepresentation for the future time point based on the future staterepresentation and the mapping function mapping state representations tofunctional representations, calculating a future value of thephysiologic signal according to the predicted functional representation,and updating the control protocol of the medical device according to thepredicted future value. The control protocol controls the medicaldevice.

Features and advantages consistent with the disclosure will be set forthin part in the description which follows, and in part will be obviousfrom the description, or may be learned by practice of the disclosure.Such features and advantages will be realized and attained by means ofthe elements and combinations particularly pointed out in the appendedclaims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF DRAWING

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIG. 1A illustrates an exemplary radiation therapy system that caninclude radiation therapy output configured to provide a therapy beam.

FIG. 1B illustrates an exemplary system including a combined radiationtherapy system and an imaging system, such as a computed tomography (CT)imaging system.

FIG. 1C illustrates a partially cut-away view of an exemplary systemincluding a combined radiation therapy system and an imaging system,such as a nuclear magnetic resonance (MR) imaging system.

FIG. 2 illustrates an exemplary collimator configuration for shaping,directing, or modulating an intensity of a radiation therapy beam.

FIG. 3A illustrates an exemplary system including a radiation therapycontroller having an imaging input, a radiation therapy generator, and aradiation therapy output.

FIG. 3B illustrates a portion of an exemplary system included in aradiation therapy controller system or an imaging system.

FIG. 4A illustrates a one-dimensional representation of an exemplarycyclic motion model, corresponding to a respiration cycle.

FIG. 4B illustrates an exemplary cyclic motion of a region of interestincluding a tumor.

FIG. 5 illustrates an example for using a cyclic motion model to predicta target locus using information indicative of an earlier target locusextracted from imaging information.

FIG. 6 illustrates an example for generating an updated therapy protocolin an adaptive manner using a cyclic motion model, and using a specifiedlatency between image acquisition of the earlier target locus and ascheduled upcoming time of therapy delivery.

FIG. 7 is a flow chart of an exemplary method for receiving imaginginformation about a time-varying target locus and generating an updatedtherapy protocol to align a therapy locus with a predicted target locus.

FIG. 8 is a flow chart of an exemplary method for establishing a cyclicmotion model, and providing a predicted target locus using the cyclicmotion model.

FIG. 9 is a flow chart of an exemplary method for signal predictionusing finite differences according to an exemplary embodiment.

FIG. 10 is a flow chart of an exemplary method for training a predictorusing finite differences according to an exemplary embodiment.

DETAILED DESCRIPTION

A radiation therapy treatment plan can be adjusted contemporaneouslywith therapy delivery in an adaptive manner, such as to compensate forcyclical changes in a position of a target locus to be treated with aradiation therapy. For example, a desired target, such as a tumor or anorgan at risk, can shift in position to such an extent that if anexclusively “offline” approach to therapy planning is used, a therapylocus of delivered radiation therapy can become misaligned with thedesired target when the radiation therapy is eventually delivered.

In one exemplary approach, imaging can be performed contemporaneouslywith delivery of a radiation therapy, such as performing an imagingacquisition just before initiating radiation therapy delivery during atherapy delivery session, or using a sequence of respective therapydelivery and imaging acquisition instances over a course of a radiationtherapy delivery session. Such imaging can provide information helpfulfor identifying a position of the target locus or for identifying motionof the target locus. Such contemporaneous imaging can be referred togenerally as “real-time,” although a latency or time delay usuallyexists between an acquisition of an image and a delivery of radiationtherapy.

Motion of the target locus can be caused by one or more sources, such asheart motion, respiration, a reflex such as a cough, or other movements.In the case of cyclic motion, such as associated with respiration, atrajectory of a target locus can be predicted using a cyclic motionmodel along with imaging information obtained regarding earlier motionof the target locus. For example, a predicted target locus for anupcoming time of therapy delivery can be generated using informationindicative of an earlier target locus extracted from the imaginginformation, along with a cyclic motion model, according to a specifiedlatency between image acquisition of the earlier target locus and thescheduled upcoming time of therapy delivery.

An updated therapy protocol can be generated in an adaptive manner toalign the therapy locus with the predicted target locus. A therapyprotocol is generally a therapy plan that the therapy delivery systemmay execute. For example, the therapy protocol can include one or moreof: (a) adjustment of one or more of actuators coupled to a moveableplatform such as a couch or table supporting the therapy recipient, (b)adjustment of one or more apertures configured to collimate or shape thetherapy beam, or (c) adjustment of one or more actuators configured toposition a therapy output to establish a specified therapy beamdirection. Such adjustment can be performed automatically before orduring therapy delivery. The adjustments may be characterized usingparameter values, trajectories, ranges, etc. Updating a therapy protocolmay involve updating such values, trajectories, or ranges.

In an illustrative example, the cyclic motion model can be establishedat least in part using a series of two or more imaging acquisitions,such as acquisitions of three-dimensional (volumetric) imaginginformation of a region. Later, such as just before therapy delivery,the time-varying target locus within the therapy recipient can beestablished using other imaging information after establishing thecyclic motion model. Such other imaging information can include one ormore of two-dimensional imaging or volumetric imaging slices including asub-region within the prior-imaged volumetric region. For example, apredicted target locus can be automatically generated by extractinginformation indicative of a feature from two-dimensional imaginginformation or imaging slices, the feature corresponding to an earliertarget locus. A phase of the cyclic motion model corresponding to thelocation of the feature can be determined. In response to determiningthe phase, a change in the location of the feature can be predictedusing a later phase of the cyclic motion model corresponding to thescheduled upcoming time of therapy delivery can be determined. Forexample, a spatial displacement in the location of the feature can bepredicted using a difference in outputs of the cyclic motion modelbetween the earlier phase and later phases.

The information indicative of the determined change in the location ofthe feature can be applied to the information indicative to the earliertarget locus to provide the predicted target locus. For example, aspatial displacement of the feature can be applied to the informationindicative of the earlier target locus, such as shifting a centroid ofthe earlier target locus to obtain a predicted target locus. The targetlocus can include a tumor, and shifting the location of the earliertarget locus to obtain the predicted target locus can include assumingthat the locus is rigid (e.g., assuming that the predicted target locusis not deformed as compared to the earlier target locus).

According to various illustrative examples described in this document,apparatus and techniques described herein can include use of a linearaccelerator (LINAC) to generate a radiation therapy beam, and use of oneor more of a computed tomography (CT) imaging system, or a nuclearmagnetic resonance (MR) imaging system to acquire the imaginginformation. Other imaging modalities and radiation therapy techniquescan be used.

It is contemplated that a radiotherapy delivery system is just oneexample of a medical system, and a therapy protocol is one example of acontrol protocol, according to which a medical system can be controlled.“A medical system,” consistent with the disclosure, may include anymedical device that measures patient data that is affected by motions.Such a medical system may be a treatment system, a surgical system, amonitoring system, or a diagnostic system. The motions may bequasiperiodic, such as cardiac motions and respiratory motions. A“control protocol” may be used to control the operation of a medicalsystem, in a way that compensates for the motions. For example, acontrol protocol may include adjustments of any components of theparticular medical system being controlled.

FIG. 1A illustrates an exemplary radiation therapy system 102 that caninclude radiation therapy output 104 configured to provide a therapybeam 108. The radiation therapy output 104 can include one or moreattenuators or collimators, such as a multi-leaf collimator (MLC) asdescribed in the illustrative example of FIG. 2. Referring back to FIG.1A, a patient can be positioned in a region 112, such as on a platform116 (e.g., a table or a couch), to receive a radiation therapy doseaccording to a radiation therapy treatment plan. The radiation therapyoutput 104 can be located on a gantry 106 or other mechanical support,such as to rotate the therapy output 104 around an axis (“A”). One ormore of the platform 116 or the radiation therapy output 104 can bemoveable to other locations, such as moveable in transverse direction(“T”) or a lateral direction (“L”). Other degrees of freedom arepossible, such as rotation about one or more other axes, such asrotation about a transverse axis (indicated as “R”).

The coordinate system (including axes A, T, and L) shown in FIG. 1A canhave an origin located at an isocenter 110. The isocenter can be definedas a location where the radiation therapy beam 108 intersects the originof a coordinate axis, such as to deliver a prescribed radiation dose toa location on or within a patient. For example, the isocenter 110 can bedefined as a location where the radiation therapy beam 108 intersectsthe patient for various rotational positions of the radiation therapyoutput 104 as positioned by the gantry 106 around the axis A.

In an example, a detector 114 can be located within a field of thetherapy beam 108, such as can include a flat panel detector (e.g., adirect detector or a scintillator detector). The detector 114 can bemounted on the gantry 106 opposite the radiation therapy output 104,such as to maintain alignment with the therapy beam 108 as the gantry106 rotates. In this manner, the detector 114 can be used to monitor thetherapy beam 108 or the detector can be used 114 for imaging, such asportal imaging.

In some embodiments, therapy beam 108 may be a kilovolts (KV) beam or amegavolts (MV) beam. Therapy beam 108 may be made up of a spectrum ofenergies, the maximum energy of which is approximately equal to thebeam's maximum electric potential times the electron charge. For an MVbeam, the maximum electric potential used by the linear accelerator toproduce the photon beam is on the megavolts level. For example, a 1 MVbeam will produce photons of no more than about 1 megaelectron-Volt(MeV).

In an illustrative example, one or more of the platform 116, the therapyoutput 104, or the gantry 106 can be automatically positioned, and thetherapy output 104 can establish the therapy beam 108 according to aspecified dose for a particular therapy delivery instance. A sequence oftherapy deliveries can be specified according to a radiation therapytreatment plan, such as using one or more different orientations orlocations of the gantry 106, platform 116, or therapy output 104. Thetherapy deliveries can occur sequentially, but can intersect in adesired therapy locus on or within the patient, such as at the isocenter110. A prescribed cumulative dose of radiation therapy can thereby bedelivered to the therapy locus while damage to tissue nearby the therapylocus is reduced or avoided.

FIG. 1B illustrates an exemplary system that can include a combinedradiation therapy system 102 and an imaging system, such as can includea computed tomography (CT) imaging system. The CT imaging system caninclude an imaging X-ray source 118, such as providing X-ray energy in akiloelectron-Volt (keV) energy range or a megaelectron-Volt (MeV) range.The imaging X-ray source 118 provide a fan-shaped and/or a conical beam120 directed to an imaging detector 122, such as a flat panel detector.The radiation therapy system 102 can be similar to the system 102described in relation to FIG. 1A, such as including a radiation therapyoutput 104, a gantry 106, a platform 116, and another flat paneldetector 114. As in the examples of FIG. 1A and FIG. 1C, the radiationtherapy system 102 can be coupled to, or can include, a high-energyaccelerator configured to provide a therapeutic radiation beam. TheX-ray source 118 can provide a comparatively-lower-energy X-raydiagnostic beam, for imaging.

In the illustrative example of FIG. 1B, the radiation therapy output 104and the X-ray source 118 can be mounted on the same rotating gantry 106,rotationally-separated from each other by 90 degrees. In anotherexample, two or more X-ray sources can be mounted along thecircumference of the gantry 106, such as each having its own detectorarrangement to provide multiple angles of diagnostic imagingconcurrently. Similarly, multiple radiation therapy outputs 104 can beprovided.

FIG. 1C illustrates a partially cut-away view of an exemplary systemthat can include a combined radiation therapy system 102 and an imagingsystem, such as can include a nuclear magnetic resonance (MR) imagingsystem 130. The MR imaging system 130 can be arranged to define a “bore”around an axis (“A”), and the radiation therapy system can include aradiation therapy output 104, such as to provide a radiation therapybeam 108 directed to an isocenter 110 within the bore along the axis, A.The radiation therapy output 104 can include a collimator 124, such asto one or more of control, shape, or modulate radiation therapy beam 108to direct the beam 108 to a therapy locus aligned with a desired targetlocus within a patient. The patient can be supported by a platform. Theplatform can be positioned along one or more of an axial direction, A, alateral direction, L, or a transverse direction, T. One or more portionsof the radiation therapy system 102 can be mounted on a gantry 106, suchas to rotate the radiation therapy output 104 about the axis A.

FIG. 1A, FIG. 1B, and FIG. 1C illustrate examples including aconfiguration where a therapy output can be rotated around a centralaxis (e.g., an axis “A”). Other radiation therapy output configurationscan be used. For example, a radiation therapy output can be mounted to arobotic arm or manipulator having multiple degrees of freedom. In yetanother example, the therapy output can be fixed, such as located in aregion laterally separated from the patient, and a platform supportingthe patient can be used to align a radiation therapy isocenter with aspecified target locus within the patient.

FIG. 2 illustrates an exemplary multi-leaf collimator (MLC) 132, forshaping, directing, or modulating an intensity of a radiation therapybeam. In FIG. 2, leaves 132A through 132J can be automaticallypositioned to define an aperture approximating a tumor 140 cross sectionor projection. The leaves 132A through 132J can be made of a materialspecified to attenuate or block the radiation beam in regions other thanthe aperture, in accordance with the radiation treatment plan. Forexample, the leaves 132A through 132J can include metallic plates, suchas comprising tungsten, with a long axis of the plates oriented parallelto a beam direction, and having ends oriented orthogonally to the beamdirection (as shown in the plane of the illustration of FIG. 2). A“state” of the MLC 132 can be adjusted adaptively during a course ofradiation therapy, such as to establish a therapy beam that betterapproximates a shape or location of the tumor 140 or other target locus,as compared to using a static collimator configuration or as compared tousing an MLC 132 configuration determined exclusively using an “offline”therapy planning technique. A radiation therapy technique using the MLC132 to produce a specified radiation dose distribution to a tumor or tospecific areas within a tumor can be referred to as Intensity ModulatedRadiation Therapy (IMRT).

FIG. 3A illustrates an exemplary system 300 including a radiationtherapy controller system 354 having an imaging input 360, a radiationtherapy generator 356, and a radiation therapy output 304. The therapygenerator 356 can include an accelerator, such as a linear accelerator,and the therapy output 304 can be coupled to the therapy generator 356to process a beam of energetic photons or particles provided by thetherapy generator 356. For example, the therapy output 304 can includeor can be coupled to an output actuator 366 to one or more of rotate ortranslate the therapy output 304 to provide a radiation therapy beamhaving a therapy locus directed to a desired target locus. The therapyoutput 304 can include a collimator 364, such as a multi-leaf collimatoras mentioned above in relation to FIG. 2. Referring back to FIG. 3A, thetherapy controller system 354 can be configured to control one or moreof the therapy generator 356, the therapy output 304, or a patientposition actuator 316 (such as a movable platform including a couch ortable), using an adaptive radiation treatment technique as described inother examples herein.

The therapy controller system 354 can be coupled to one or more sensors,such as using a sensor input 362. For example, a patient sensor 358 canprovide physiologic information to the therapy controller system, suchas information indicative of one or more of respiration (e.g., using aplethysmographic sensor), patient cardiac mechanical or electricalactivity, peripheral circulatory activity, patient position, or patientmotion. Such information can provide a surrogate signal correlated withmotion of one or more organs or other regions to be targeted by thetherapy output 304.

The imaging input 360 can be coupled to an imaging system 350 (such ascan include a computed tomography imaging system or a nuclear magneticresonance (MR) imaging system, as illustrative examples). Alternatively,or in addition, the therapy controller system 354 can receive imaginginformation from an imaging data store 352, such as a centralizedimaging database or imaging server. One or more of the therapycontroller system 354 or the imaging system 350 can include elementsshown and described in relation to the system 396 shown in FIG. 3B.

FIG. 3B illustrates a portion of a system 396 including elements of aradiation therapy controller system 354 or an imaging system 350. Thesystem 396 can include a main memory circuit 378 into which executableinstructions or other data can be loaded, and a processor circuit 370 toexecute or otherwise perform such instructions. The system 396 caninclude a static memory circuit 376, such as to provide a cache or otherstructure to store data related to a currently-executing series ofinstructions. A read-only memory (ROM) circuit 374 can permanently storeinstructions such as to facilitate a boot sequence for the system 396 orto facilitate operation of hardware devices attached to the system 396.

The system 396 can include a bus circuit 398 configured to conveyinformation between elements or circuits comprising the system 396. Forexample, a drive unit 372 can be included or attached to the server,such as to store instructions related to the radiation therapy planning,imaging, or radiation therapy delivery techniques as mentioned elsewhereherein. The system 396 can include one or more of a display 386 such ascan include a bit-field or alphanumeric display, an alpha-numericcontrol 384 such as a keyboard, or a cursor control device 382 such as atouch screen, touch pad, trackball, or mouse. Examples of systems thatcan use or include elements of the system 396 include one or more of thetherapy controller system 354, the imaging system 350, or a treatmentplanning system.

The system 396 can be connected to a centralized network 390 (e.g., alocal area network, an “intranet,” or a wide area network such as theInternet), such as to store or retrieve information from a server 394(e.g., such as a server housing an imaging information database, aradiation therapy treatment plan, or other information such as a patientmedical record). For example, the system 396 can include one or morewired or wireless interface circuits, such as a network interfacecircuit 380 configured to provide access to other systems such as tofacilitate exchange of imaging or radiation therapy control information.

The system 396 can describe an embedded controller included as a portionof other apparatus, a personal computer (PC), a tablet device, or acellular communications device such as a “smart” cellular device, asillustrative examples. While a single processor circuit 370 is shownillustratively in FIG. 3B, a plurality of processor circuits, “cores,”or machines can be used, such as can individually or jointly perform aset (or multiple sets) of instructions to perform any one or more of thetechniques described herein, such as instructions stored on aprocessor-readable medium (also referred to as a computer-readablemedium).

Illustrative examples of a processor-readable medium include solid-statememories, optical, or magnetic media. For example, solid-state memoriescan include one or more of a read-only memory (ROM), a flash memory, adynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) orRambus DRAM (RDRAM and the like)), or a static memory (e.g., flashmemory, static random access memory (SRAM, or the like)).

The processor circuit 370 can include one or more processing circuitssuch as a microprocessor, a central processing unit, or the like. Inparticular, the processor can include a complex instruction setcomputing (CISC) architecture microprocessor, a reduced instruction setcomputing (RISC) architecture microprocessor, or a very long instructionword (VLIW) architecture microprocessor. According to other examples,the processor circuit 370 can include one or more of a microcontroller,an application specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), a networkprocessor, or a system-on-chip (SoC) circuit. For example, amicrocontroller can include one more integrated circuits having a memorycircuit and processor circuit co-integrated within a single devicepackage.

Motion Prediction for Adaptive Radiation Therapy Using Imaging and aCyclic Motion Model

As mentioned above, a radiation therapy treatment plan can be adjustedcontemporaneously with therapy delivery in an adaptive manner, such asto compensate for cyclical changes in a position of a target locus to betreated with a radiation therapy. For example, a desired target, such asa tumor, can shift in position to such an extent that if an exclusively“offline” approach to therapy planning is used, a therapy locus ofdelivered radiation therapy can become significantly misaligned with thedesired target when the radiation therapy is eventually delivered.Motion of the target locus can be caused by one or more sources, such asheart motion, respiration, a reflex such as a cough, or other movements.In the case of cyclic motion, such as associated with respiration, atrajectory of a target locus can be predicted using a cyclic motionmodel along with imaging information obtained regarding earlier motionof the target locus. The techniques described below can be implementedin whole or in part using, or can use, the systems described above inrelation to one or more of FIG. 1A, FIG. 1B, FIG. 1C, FIG. 2, FIG. 3A,or FIG. 3B.

FIG. 4A illustrates a one-dimensional representation of a cyclic motionmodel 400, corresponding to physiologic cycle 410 (e.g., a respirationcycle), such as defined over a duration extending from a time, 0, totime “T,” where T represents a period of the cyclic motion. The motionmodel 400 can represent an absolute or relative spatial position ordisplacement, such as modeling an absolute or relative trajectory of anorgan as function of time, f(t). The trajectory can representtime-varying motion of a feature corresponding to the organ. In someembodiments, the feature can be a reliably-identifiable point on theorgan, such as a centroid of the organ. In an illustrative example wherethe cyclic motion model represents respiration, at time “0” the functionf(0) can define a beginning-of-inspiration (BOI) reference datum 402.The function also defines an end-of-inspiration (EOI) orbeginning-of-expiration (BOE) reference datum 404. At time T, thefunction f(T) can define an end-of-expiration (EOE) datum 406.

In the illustrative example of FIG. 4A, the motion model f(t) is shownas a scalar function of time having a single displacement dimension.However, such a model is merely illustrative and other examples caninclude a cyclic motion model defined by multiple spatial displacementfunctions. For example, in a Cartesian coordinate system, such spatialdisplacement functions comprising the cyclic motion model can berepresented by functions x(t), y(t), and z(t). One or more of thereference locations (e.g., BOI, EOI, BOE, EOE) defined in FIG. 4A can bedefined within one or more of x(t), y(t), and z(t). Accordingly, thecyclic motion model can be developed for each dimensional component.While various examples herein refer generally to continuous timemathematical functions, an automated implementation of techniquesdescribed herein can include using a discretized representation of thecyclic motion model (e.g., a sampled or discrete representation of themodel values corresponding to discrete time or discrete phase values).

FIG. 4B illustrates a cyclic motion of a region of interest 416A.According to various embodiments, the region of interest 416A caninclude an organ, a portion of an organ, or a tumor. A series of two ormore imaging acquisitions can be performed, using one or more imagingtechniques to obtain imaging information representative of the locationof the region of interest 416A. For example, one or more of computedtomography (CT) or nuclear magnetic resonance (MR) imaging can be used,such as in an “offline” manner, to establish a location of the region ofinterest 416A.

The location of the region of interest 416A can be established from theimaging information at least in part using one or more of manualdelineation of a boundary of the region of interest 416A, or anautomated segmentation technique. For example, segmentation can includeassigning one or more pixels or voxels from acquired imaging informationas being members of the set corresponding to the region of interest416A. Segmentation can include determining a contrast value of one ormore pixels or voxels and comparing the contrast value to a criterion(e.g., a contrast threshold), such as to assist group the one or morepixels or voxels as members of the set containing the region ofinterest. A feature can then be extracted from the segmented region ofinterest 416A, such as a centroid location 408A.

The region of interest 416A can move in a time-varying fashion, such asto a location corresponding to a displaced region of interest 416B,having a correspondingly-displaced centroid 408B. Information about adisplaced region of interest 416B and the correspondingly-displacedcentroid 408B can also be extracted from one or more images in theseries of two or more imaging acquisitions mentioned above. In thismanner, a series of two or more images can be used to describe anabsolute or relative trajectory of a feature, such as the determinedcentroid 408A and centroid 408B locations. In an illustrative example,the series of two or more images comprises a series of volumetric images(e.g., a “4D” series of images including three spatial dimensionsacquired at different times). Other types of imaging can be used, suchas one-dimensional imaging, or two-dimensional imaging or imaging slicesextending in two dimension and also having a finite depth.

The use of a centroid as an extracted feature is illustrative. Otherfeatures can be used, such as a manually-identified orautomatically-determined location of a point or surface (e.g., an edge412A of the region-of-interest 416A and a displaced edge 4128 of thedisplaced region of interest 416B). In yet another example, a motion ofan implantable or external seed fiducial can be tracked, such using amotion of an indicium 414A (that can represent a seed location at afirst time) in the imaging information corresponding to a firstlocation, and a displaced indicium 4148 corresponding to the displacedlocation (that can represent the seed location at a later time). Othertechniques can be used to track displacement, such as use of an MRimaging “navigator echo,” such as assigned to a location near an edge ofan anatomical feature nearby or included as a portion of the region ofinterest.

FIG. 5 illustrates an exemplary cyclic motion model 500 to predict atarget locus using information indicative of an earlier target locusextracted from imaging information. Imaging information can be obtainedcontemporaneously with radiation therapy delivery, such as to adjust aradiation therapy protocol to adaptively compensate for motion of aradiation therapy target locus. For example, imaging information can beobtained just before therapy delivery to be used in determining apredicted target locus. Information about one or more acquired imagescan be used to align an instance of an imaging acquisition with aportion of the cyclic motion model, such as including determining arelative time between a reference datum such as a datum 504(corresponding to a time t₀), and a time, t₁, corresponding to anacquired image instance. In the illustrative example of a respirationmodel, the datum 504 can correspond to an end of inspiration (EOI) or abeginning of expiration (BOE) and can be detected such as by analyzing agradient of respiration-related information from information extractedfrom a series of images corresponding to a complete respiratory cycle,or using surrogate information obtained from another sensor (e.g., aplethysmographic sensor).

A scheduled upcoming therapy delivery time can occur at t₂. Accordingly,a predicted target location can be generated corresponding to time t₂.In an example, the cyclic motion model can be evaluated at a timecorresponding to t₁, and a time corresponding to t₂=t₁+□. The variable,□, can represent a specified latency, such as between a timecorresponding to an earlier image acquisition at time t₁, and ascheduled upcoming therapy delivery at t₂.

In one approach, the cyclic motion model 500 can represent an absoluteposition of a feature of interest, such as a centroid of a target locuscorresponding to a tumor or an organ, and the value of the cyclic motionmodel evaluated at time t₂ can be used directly as the predicted featurelocation at the time of therapy delivery. However, such an approach canhave drawbacks, such as leading to inaccurate motion prediction if thepatient is repositioned or is unable to be positioned in the same manneras when the imaging information was first acquired and used to developthe cyclic motion model. By contrast, a difference in the values of thecyclic motion model can be used to estimate a relative displacement ofan imaging feature, and such a relative displacement can be used toadjust the location of the imaging feature to determine a predictedfeature location at a later time, particularly over short time scaleswhere the latency □ is relatively small with respect to the overallcycle length. Accordingly, motion prediction performed using a change infeature position derived from a cyclic motion model can be insensitive,at least to a first order, to variations in target motion between theabsolute position predicted by the model and the actual target positionobserved using imaging.

As an illustrative example, a difference between values of the cyclicmotion model can be determined, such as can be represented byf(t₁+□)−f(t₁). Such a determined difference can then be used to adjust afeature location obtained from the imaging information. For example, ifan imaging feature location of an acquired image corresponding to timet₁ is represented by I_(f)(t₁), then the predicted feature location attime t₂ can be represented by Î_(f)(t₂)=I_(f)(t₁)+[f(t₁+□)−f(t₁)]. Inthis manner, the cyclic motion model need not accurately predict afeature location in an absolute sense, but the cyclic motion model canprovide a useful estimate of a change in the feature location (e.g., arelative spatial displacement) when the time corresponding to the imageacquisition is aligned with the appropriate location in the cyclicmodel.

Alignment of the time corresponding to the image acquisition can includedetermining a time elapsed between a reference point in a physiologiccycle of a patient and a time at which an image is acquired. Forexample, a cycle phase can range from a t-value of 0 to T, where T canbe the total cycle duration, such as period corresponding to a breathingcycle. A fraction of the total cycle duration (e.g., a percentage) canbe determined using an expression, phase=(100 t)/T. In such anexpression, phase can represent a phase-percentage of the cycle at acycle phase corresponding to time t. T can be presented in units of time(e.g., seconds or milliseconds). T can be empirically determined, suchas by averaging a duration between one or more reference points or usingone or more other techniques to determine a central tendency of a seriesof cycle duration values. In an illustrative example relating torespiration, use of cycle reference points can include measuring aseries of durations between end-of-expiration (EOE) orend-of-inspiration (EOI) over one or more breathing cycles to estimateT.

Use of “phase” rather than an absolute time can allow use of a cyclicmotion model that is at least somewhat scale invariant over the timedimension because such a phase is generally dimensionless. For example,an actual breathing cycle period of a patient will generally vary inabsolute duration from cycle-to-cycle. Use of phase to describe anacquisition time of an image relative to a reference point along thecycle allows alignment of a phase corresponding to the acquisition timewith the appropriate location along the cyclic motion model, where thephase expresses a percentage of the total cycle length, even if theabsolute cycle length corresponding to acquired imaging informationdiffers from cycle-to-cycle.

The cyclic motion model can be described in three dimensions as shownillustratively below. A feature, such as centroid location, can beextracted from an earlier-acquired image acquired at time t₁, where thetime t₁ is determined by aligning the earlier-acquired image with theappropriate location (phase) along the cycle. The extracted centroidlocation can be represented in three dimensions as [x_(c)(t₁),y_(c)(t₁), z_(c)(t₁)]. The cyclic motion model can be represented bythree functions of time, x(t), y(t), and z(t). Accordingly, in threedimensions, a predicted target locus at time t₂ can be determined asfollows:{circumflex over (x)}(t ₂)={circumflex over (x)}(t ₁+□)=x _(c)(t ₁)+[x(t₁+□)−x(t ₁)]  [EQN. 1]{circumflex over (y)}(t ₂)={circumflex over (y)}(t ₁+□)=y _(c)(t ₁)+[y(t₁+□)−y(t ₁)]  [EQN. 2]{circumflex over (z)}(t ₂)={circumflex over (z)}(t ₁+□)=z _(c)(t ₁)+[z(t₁+□)−z(t ₁)]  [EQN. 3]

The centroid location from the earlier-acquired image can be determinedusing a variety of techniques. For example, a contrast between adjacentpixels or voxels in an earlier-acquired image can be used to delineate aboundary of a region such as the target locus in the earlier-acquiredimage. A spatial centroid location can then be determined based upon thedelineated boundary. Other approaches can be used, such as includingevaluating contrast between adjacent pixels or voxels to automaticallysegment the boundary, or using other techniques such as edge detection.

Sources of latency contributing to the latency variable, □□ can includeimage acquisition latency, image processing latency such ascorresponding to operations including segmentation, registration, ordata transfer of imaging information, radiation therapy system latencysuch as corresponding to computation latency in execution of a motionprediction technique, or a latency related to therapy adjustment. Suchtherapy adjustment can include latencies associated with of one or moreof positioning of a patient platform, positioning a radiation therapyoutput, or configuring an aperture-defining element such as acollimator. The latency, □, need not be fixed and can be measured,manually configured, or automatically determined before the estimate isperformed.

As an illustrative example, a combined MR-imaging and linac systemlatency can be on the order of a fraction of a second, such as around100 milliseconds, between an imaging acquisition for purposes of targetlocation prediction, and a subsequent delivery of radiation therapy. Forsuch a system latency, which is on the order of a fraction of a second,the target locus can be modeled as rigid. Accordingly, a change in thelocation of the centroid such as provided by a cyclic motion model canbe applied to an earlier-identified target locus without requiringdeformation of the earlier target locus. For example, the earlier targetlocus can be spatially translated by a displacement corresponding to thedetermined change in centroid location predicted by the cyclic motionmodel. In this manner, a predicted target locus can be provided for usein delivery of radiation therapy.

FIG. 6 illustrates an example 600 for generating an updated therapyprotocol in an adaptive manner using a cyclic motion model, and using aspecified latency between image acquisition of the earlier target locusand a scheduled upcoming time of therapy delivery. An earlier targetlocus can be identified at 602, such as using a series of acquiredimages. The images can include one or more of volumetric imaginginformation acquired over time (such to provide four-dimensional imaginginformation), or three-dimensional imaging information such as sliceshaving a finite depth as shown illustratively in FIG. 6, or twodimensional imaging information, according to various illustrativeexamples. In an example, two dimensional imaging information orthree-dimensional slices can be acquired, such as to provide a series ofrapidly-acquired images over a duration of a portion or an entirety of aphysiologic cycle such as a respiration cycle. At target locus 616A,such as a tumor, can be identified in a first location in an acquiredimage portion 630A (e.g., an imaging slice), and the target locus 616Bcan vary in position over time as shown in a later-acquired imageportion 630B. The time-varying position of the target locus can betracked, such as throughout the series of images acquired at 602, and apredicted target locus 616C can be determined within a region ofinterest 630C corresponding to a future scheduled time of therapydeliver.

The predicted target locus 616C can be provided such as by determining atime or phase of the acquisition of one or more images acquired at 602in relation to a cycle described by a cyclic motion model as discussedabove in relation to FIG. 4A, FIG. 4B, and FIG. 5, and adjusting anearlier-acquired target locus, such as the target locus 616B using adetermined displacement provided by the cyclic motion model. A therapylocus 620 can then be aligned with the predicted target locus 616C fortherapy delivery. In this manner, the therapy locus 620 can beadaptively aligned with a time-varying target locus such as a tumor. Thetherapy locus 620 refers to a region of tissue to be targeted by aradiation therapy beam provided by a radiation therapy output 104.

FIG. 7 is a flow chart of an exemplary method 700 for receiving imaginginformation at 702. The imaging information can include informationextracted from one or more images, the information indicative of atime-varying target locus, such as a tumor, an organ, or a portion of atumor or organ. The target locus can represent a region of tissue withinpatient to be targeted by radiation therapy. At 704, a predicted targetlocus can be generated, such as corresponding to a scheduled upcomingtime of therapy delivery. For example, the predicted target locus can bedetermined using information indicative of an earlier target locus and acyclic motion model, as shown and described in examples mentionedelsewhere in this document.

The time of the scheduled upcoming therapy delivery corresponding to thepredicted target locus can be determined at least in part usinginformation about a specified latency between a time of an imageacquisition of the earlier target locus and the scheduled upcoming timeof therapy delivery. At 706, an updated therapy protocol can begenerated, such as including aligning a radiation therapy locus with thepredicted target locus. In this manner, the therapy locus of deliveredradiation therapy is aligned with the moving target locus.

FIG. 8 is a flow chart of an exemplary method 800. At 802, two or moreacquisitions of imaging information from a region of interest arereceived. For example, at 802 the acquisitions can include receivingimaging information corresponding to two or more acquisitions ofthree-dimensional imaging information, such as acquired using one ormore of an MR imaging or CT imaging technique. At 804, a target locuscan be identified within the imaging information corresponding to thetwo or more acquisitions. For example, the target locus can beidentified through a segmentation technique as mentioned in relation toother examples described in this document.

At 806, information about a motion of the target locus can be extracted.Such information can include a spatial location of one or more featurescorresponding to the target locus, such as an edge or a centroidlocation. A change in location of the feature across the two or moreimage acquisitions can be determined. In response, at 808, a cyclicmotion model can be established, such as comprising a spatialdisplacement model of the motion of the target locus in at least onedimension, such as a function of time or phase. In an example, theacquired imaging information and extracted information about the motionof the target locus can span several cycles, such as several physiologiccycles. As an illustration, imaging information can be obtained at 802corresponding to one or more complete respiration cycles, and the cyclicmotion model established at 808 can include aggregating informationobtained from the obtained information into a composite, using averagingor other techniques. The series of operations at 802, 804, 806, and 808can be performed “offline,” such as well in advance of a scheduledradiation therapy treatment session (e.g., days or weeks beforetreatment). Alternatively, or in addition, the series of operations at802, 804, 806, and 808 can be performed the same day as a scheduledradiation therapy treatment session, such as hours or minutesbeforehand.

At 810, after establishing the cyclic motion model, imaging informationcan be received about the time varying target locus. For example,acquisition of images for use at 810 can be performed contemporaneouslywith therapy delivery, such as within seconds or even fractions ofseconds before a scheduled instance of radiation therapy delivery. At812, information indicative of a feature corresponding to an earliertarget locus can be extracted from the imaging information received at810. As shown and described elsewhere in this document, the feature caninclude a centroid, an edge, an indicium corresponding to an external orimplantable seed, or an MR navigator echo, as illustrative examples. At814, a phase of a cyclic motion model corresponding to the location ofthe feature can be determined. At 816, a change in the location of thefeature can be estimated using a later phase of the cyclic motion modelcorresponding to the scheduled upcoming time of therapy delivery. At818, the information determined at 816 regarding the change in locationof the feature can be applied to the information indicative of theearlier target locus to provide the predicted target locus. In thismanner, the therapy locus is adaptively aligned with the predictedtarget locus to one or more of (a) better align the radiation beam witha tissue target such as a tumor for treatment and (b) avoid or minimizedamage to tissue or organs adjacent to the tissue target.

An imaging modality (e.g., MR, CT, PET, SPECT) or imaging representation(e.g., one-dimensional, two-dimensional, three-dimensional) used forestablishing the cyclic motion model need not be the same as the imagingmodality or representation used for extracting the informationindicative of the feature, corresponding to the earlier target locus.For example, detailed high-resolution imaging information in threedimensions may be used for developing the cyclic motion model, in an“offline” fashion. Then, just before or during a radiation therapydelivery, imaging information may be acquired using a higher-speedtechnique, such as including higher frame rate or a shorter acquisitionlatency as compared to the imaging approach used for developing thecyclic motion model. In this sense, the image acquisitions correspondingto imaging information received at 810 can be referred to as occurringin “real time” relative to therapy delivery, even though such imagingneed not be acquired literally simultaneously during application of thetherapy beam.

Finite-Differences Based Prediction

In addition to the above-described prediction using cyclic model, otherpredication techniques can be used, such as non-model-based predictiontechniques. In this section, embodiments of finite differences-basedmotion prediction techniques are described. The techniques describedbelow in this section can be implemented in whole or in part using, orcan use, the medical systems described above in relation to one or moreof FIG. 1A, FIG. 1B, FIG. 1C, FIG. 2, FIG. 3A, or FIG. 3B. The examplesdescribed in this and next sections relate to the prediction of futurevalues of a quasiperiodic physiologic signal, also referred to herein asa physiologic signal, reflecting the quasiperiodic motion of a certainregion or organ in a human body, such as a respiratory signal reflectingthe quasiperiodic motion of lungs. As indicated above, such aquasiperiodic motion can affect the locus of a target. Therefore, thepredicted future values of the quasiperiodic physiologic signal can beused to, for example, update a therapy protocol of a therapy generatorthat generates a therapy beam to be directed to a locus within a therapyrecipient, i.e., a patient. As another example, the predicted futurevalues of the quasiperiodic physiologic signal can be used to align animaging system with a locus on a target, such as a patient for whom theimage is taken.

In general, the physiologic signal can be a multivariate signal, i.e.,can vary in multiple dimensions, such as in three dimensions, and thusis a vector. In some scenario, the physiologic signal includes variationin only one dimension and thus is a scalar. In the present disclosure,the physiologic signal is represented generally as a vector form, x(t),but it is to be understood that the physiologic signal can be a scalar.To simplify discussion, in the examples described in this and nextsections, the parameter t, although representing time, takes indexvalues, such as 0, 1, 2, . . . , instead of absolute time values, andthus is also referred to herein as a “time index” or a “time step.” Theactual time value between two time steps may depend on the instrumentthat measures the physiologic signal.

According to the present disclosure, finite differences of thephysiologic signal are used as the regular variable and a firstdifference between a current value of the physiologic signal at a time tand a future value of the physiologic signal at a future time t+δ, i.e.,x(t+δ), is used as the target variable. That is, the first difference,i.e., the target variable, y(t), for time t in thefinite-differences-based motion prediction described in this section isdefined as:y(t)=x(t+δ)−x(t)  [EQN. 4]which is also referred to herein as a “difference value.” The differencevalue is indicative of a difference between a future time t+δ and acurrent time t. In the present disclosure, the parameter δ is alsoreferred to as a “prediction horizon,” which represents a time span fromthe current time to the future time for which the prediction isconducted. Like the time t, the prediction horizon δ can also take anindex value or an absolute time value. In the examples discussed in thisand next sections, to simplify discussion, the prediction horizon δ alsotakes an index value.

According to the disclosure, a set of derivatives of the physiologicsignal evaluated at time t, from the first order to higher orders(x′(t), x″(t), x′″(t), . . . , can be used as the regular variable. Inpractice, however, finite differences of the physiologic signal at timet are used to approximate the derivatives since finite differences pasttime t are not known and the physiologic signal is discrete, i.e.,sampled at discrete times. The set of finite differences of thephysiologic signal at time t, i.e., the regular variable, can berepresented by a differences signal d(t, p, o), defined as follows:

$\begin{matrix}{{d\left( {t,p,o} \right)} = \left( \begin{matrix}{{x(t)} - {x\left( {t - p_{1}} \right)}} \\{{x(t)} - {2{x\left( {t - p_{1}} \right)}} + {x\left( {t - \left( {p_{1} + p_{2}} \right)} \right)}} \\{{x(t)} - {3{x\left( {t - p_{1}} \right)}} + {3{x\left( {t - \left( {p_{1} + p_{2}} \right)} \right)}} - {x\left( {t - \left( {p_{1} + p_{2} + p_{3}} \right)} \right)}} \\\vdots \\{{x(t)} + {\sum\limits_{j = 1}^{o}{\left( {- 1} \right)^{j}\begin{pmatrix}o \\j\end{pmatrix}{x\left( {t - {\sum\limits_{h = 1}^{j}p_{h}}} \right)}}}}\end{matrix} \right)} & \left\lbrack {{EQN}.\mspace{14mu} 5} \right\rbrack \\{\mspace{79mu}{where}} & \; \\{\mspace{79mu}{\begin{pmatrix}o \\j\end{pmatrix} = \frac{o!}{{j!}{\left( {o - j} \right)!}}}} & \left\lbrack {{EQN}.\mspace{14mu} 6} \right\rbrack\end{matrix}$and the parameter o represents the order of differences included in thedifferences signal and thus controls the size of the differences signal.The parameter o is also referred to herein as a “differences signalscale.” The value of the differences signal scale o can depend onvarious factors, such as the physiologic signal, the application, andthe prediction horizon δ.

Thus, according to the present disclosure, in addition to the firstorder finite difference, at least one high-order finite difference,i.e., a finite difference of the second or higher order, are used forthe prediction. In EQN. 5, four finite differences, i.e., the first,second, third, and o-th order finite differences are shown. However, itis to be understood that EQN. 5 is a general representation of thedifferences signal, which is not limited to the particular finitedifferences in EQN. 5. For example, the differences signal can includethe first and second order finite differences of the physiologic signal.In some embodiments, the differences signal includes the first, second,and third, or additionally higher-order finite differences of thephysiologic signal. With the high-order finite differences used in theprediction according to the present disclosure, future values of thephysiologic signal can be predicted more accurately.

The vector p=[p₁, p₂, . . . , p_(o)]^(T) in EQN. 5 is also referred toherein as a “step-size vector.” Each component in this vector controlsthe step-size used for taking the finite difference of a correspondingorder. For example, p₁ represents the step size for taking the firstorder finite difference of the physiologic signal and is thus alsoreferred to herein as a “first order step size,” p₂ represents the stepsize for taking the second order finite difference of the physiologicsignal and is thus also referred to herein as a “second order stepsize,” and p_(o) represents the step size for taking the o-th orderfinite difference of the physiologic signal and is thus also referred toherein as an “o-th order step size.” The step sizes p₁, p₂, . . . ,p_(o) are chosen based on various factors such as the characteristics ofthe physiologic signal, the application, and the measurement of thephysiologic signal. In some embodiments, the step sizes p₁, p₂, . . . ,p_(o) can depend on the value of the prediction horizon δ. According tothe present disclosure, p₁, p₂, . . . , p_(o) can be the same as ordifferent from each other. For example, p₁, p₂, . . . , p_(o) can allequal 1.

The regular/target variable pair

d(t, p, o), y(t)

defined above can be used with any suitable prediction algorithms topredict the future values of the physiologic signal. In the presentdisclosure, the prediction algorithm used with the variable pair

d(t, p, o), y(t)

to predict future values is also referred to herein as a predictor.Various prediction algorithms can be used in conjunction with thevariable pair consistent with embodiments of the present disclosure. Theprediction algorithm may be, for example, support vector regression,non-parametric probability-based methods such as kernel densityestimation, or linear regression.

Before the above-described predictor can be used to predict futurevalues of the physiologic signal, the predictor would be trained todetermine proper predictor parameter values. Consistent with the presentdisclosure, historical, measured values of the physiologic signal,{x(k):k=0, 1, . . . , n}, can be used to train the predictor.Accordingly, the disclosed methods may be suitable to work on the fly,and adaptive to baseline shifts. Here, k takes an index value andrepresents the time at which a measurement is conducted and a value ofthe physiologic signal is obtained. In this example, a total of n+1measurements are conducted, each of which corresponds to one-time indexk.

As discussed above, for a certain time k′, a future value of thephysiologic signal at a future time k′+δ can be predicted. To train thepredictor, both the regular variable, i.e., the differences signal, andthe target variable, i.e., the difference value, need to be known.Therefore, in this example, the highest value for k′ can be n−δ. This isbecause, for example, if k′ equals n−δ+1, then the future timecorresponding to time k′ is n+1, but there is no measured data for timepoint n+1, and thus no difference value y(k′) can be calculated for timek′=n−δ+1 for the training purpose. Further, according to EQN. 5, tocalculate the differences signal for time k′, data points in the rangefrom time (k′−Σ_(h=1) ^(o)p_(h)) to time k′ are needed. Thus, since inthis example the lowest time index k is 0, the lowest value for k′should be Σ=_(h=1) ^(o)p_(h). Therefore, in this example, the value ofk′ can be Σ=_(h=1) ^(o)p_(h)+1, . . . , n−δ.

For example, assume 20 data points of the physiologic signal aremeasured, i.e., a data set {x(k):k=0, 1, . . . 19} is obtained. Furtherassume that the differences signal includes three finite differences,i.e., o=3 and p=[p₁, p₂, p₃]^(T) with p₁=p₂=p₃=1, and that δ=2. Then thevalue of time k′ can be 3, 4, . . . , 17.

With the values of K determined as described above, a training variablepair

d(k′, p, o), y(k′)

can be calculated for each k′, with k′=Σ=_(h=1) ^(o)p_(h), Σ_(h=1)^(o)p_(h)+1, . . . , n−δ. The obtained training variable pairs

d(k′, p, o), y(k′)

can then be used to train the predictor to obtain proper predictorparameter values.

After the predictor is trained, the predictor can be used to predict afuture value of the physiologic signal based on any current value of thephysiologic signal at a current time t_(c). First, for the current timet_(c), the differences signal d(t_(c), p, o) is calculated using EQN. 5.The calculated differences signal d(t_(c), p, o) is then substitutedinto the predictor as the regular variable to predict the targetvariable, i.e., the difference value ŷ(t_(c)), which is then used tocalculate the predicted value of the physiologic signal at the futuretime t_(c)+δ according to {circumflex over(x)}(t_(c)+δ)=ŷ(t_(c))+x(t_(c)) (the hat symbol “^” above symbols x andy indicates they are predicted values). As discussed above, thepredicted future value {circumflex over (x)}(t_(c)+δ) can be used to,for example, predict a target locus on a therapy recipient of a therapydelivery system and to update a therapy protocol used by a therapygenerator of the therapy delivery system, such as including aligning aradiation therapy locus with the predicted target locus.

FIG. 9 shows a flow chart of an exemplary method 900 consistent withembodiments of the present disclosure for predicting a future value of aphysiologic signal. As shown in FIG. 9, at 902, a measured data setcontaining a current value of the physiologic signal at a current timeand past values of the physiologic signal at times before the currenttime is received. The physiologic signal can be, for example, arespiratory signal reflecting the quasiperiodic motion of the lungs of atarget, such as a therapy recipient. At 904, a differences signal at thecurrent time is calculated using the measured data set. The calculationcan be conducted using, for example, EQN. 5. At 906, a predicteddifference value is calculated by substituting the differences signalinto a predictor. At 908, a predicted future value of the physiologicsignal is calculated based on the predicted difference value and thecurrent value of the physiologic signal. At 910, the predicted futurevalue of the physiologic signal is used to update a protocol forcontrolling a medical device. The medical device can be, for example, animaging system for taking images of, or a therapy delivery system fordelivering therapy to, an object, such as a patient. The protocol can beused to, for example, control the alignment of the medical device. Thus,the predicted future value can be used to, for example, align theimaging system or a therapy generator of the therapy delivery systemwith a target locus on the object. For example, the predicted futurevalue can be used to update a therapy protocol of the therapy generatorof the therapy delivery system, where the therapy protocol is used tocontrol the therapy generator to direct the therapy beam.

FIG. 10 shows a flow chart of an exemplary method 1000 consistent withembodiments of the present disclosure for training a predictor used forpredicting future values of a physiologic signal. As shown in FIG. 10,at 1002, a measured data set containing values of the physiologic signalmeasured at a plurality of time points is received. At 1004, adifferences signal at one or more of the time points is calculated basedon the measured data set, to obtain one or more training variable pairseach containing the calculated differences signal at the time point anda difference value corresponding to the time point. Hereinafter, adifferences signal calculated during training is also referred to as a“training differences signal” and a difference value calculated duringtraining is also referred to as a “training difference value.” At 1006,the one or more training variable pairs are used to train the predictorto obtain proper parameter values for the predictor.

The exemplary methods shown in FIGS. 9 and 10, although describedseparately above, can be combined together consistent with the presentdisclosure. For example, the exemplary method 1000 can be conductedfirst to train a predictor using historical measured values of thephysiologic signal, and then the trained predictor is used to predictthe future values of the physiologic signal according to the exemplarymethod 900. Consistent with some embodiments, the predictor may bere-trained one or more times during any suitable point of method 900 toadapt to drift in the breathing behavior. The retraining may beperformed on the fly and provide updated parameter values for thepredictor. A predictor so adaptively trained on the fly can bettercompensate for the baseline shifts.

Using the methods consistent with the present application, theprediction performance of each prediction algorithm can be improved ascompared to the results without using the finite differences, and issignificantly improved as compared to the results without anyprediction.

Prediction Based on Model Regression

Both model-based methods, such as Kalman filter, and model-free methods,such as normalized least mean-square filter and regression methods, canbe used to predict future values of a physiologic signal. Model-basedmethods predict the physiologic signal in a context. Therefore, inaddition to the future values of the physiologic signal, the model-basedmethods can also predict an internal state of an anatomical structure,i.e., the source of the quasiperiodic motion that generates thephysiologic signal. For example, the anatomical structure may be aregion on or an organ of a human body, such as a patient or a therapyrecipient. As such, the physiologic processes of the anatomicalstructure can be inferred from the information about the internal stateof the anatomical structure. Moreover, by knowing the internal state ofthe anatomical structure and its relationship with the physiologicsignal, the signal can be predicted at different prediction horizonswithout changing the parameters of the predictor.

On the other hand, model-free methods use numerical methods to determinea mapping from a current set of measured or observed values of thephysiologic signal to expected future values of the physiologic signal,and are thus more flexible towards changes in the characteristics of thephysiologic signal.

In this section, motion prediction techniques based on model regressionis described. The model regression methods consistent with the presentdisclosure apply model-free prediction methods to determine the model ofthe anatomical structure, and thus benefit from the advantages of boththe model-based and model-free prediction methods. The techniquesdescribed below in this section can be implemented in whole or in partusing, or can use, the medical systems described above in relation toone or more of FIG. 1A, FIG. 1B, FIG. 1C, FIG. 2, FIG. 3A, or FIG. 3B.

As discussed above, the physiologic signal x(t) is a function of time.In this section, x(t) is also denoted as:x(t)=ƒ(.)(t)  [EQN. 7]where f(.) denotes a functional representation that can be used tocalculate values, such as future values, of the physiologic signal. Thefunctional representation takes time t as input and output the values ofthe physiologic signal at time t, i.e., x(t). The functionalrepresentation can be determined according to the model regressionmethods of the present disclosure. Specifically, the relationshipbetween the functional representation of the physiologic signal x(t),i.e., f(.), and the internal state at time t can be represented asfollows:ƒ(.)=ƒ(s _(t))(t)  [EQN. 8]where s_(t) denotes a state representation representing specific resultsof the internal state s(t) of the anatomical structure at time t, andƒ(s_(t)), hereinafter also referred to as a “mapping function,” denotesa mapping from the state space to the functional representation of thephysiologic signal, f(.). The state representation s_(t) may be a vectoror a scalar. When the state representation s_(t) is a vector, it is alsoreferred to as a “state vector.” The internal state s(t), and thus thestate representation s_(t), are particular to the model used, and can bedifferent for different models. Thus, not only the physiologic signalx(t) is a function of time, the functional representation f(.) used tocalculate the physiologic signal x(t) is also a function of time anddepends on the state representation s_(t). That is, the functionalrepresentation f(.) can change with time.

Therefore, according to the present disclosure, the particular type andform of the functional representation f(.) at time t depend on theinternal state s(t) and are determined by the state representation s_(t)at time t according to the mapping function ƒ(s_(t)). For example,depending on the internal state, the functional representation f(.) canbe a linear function, a quadratic function, or a cubic function, with aparticular set of coefficients. The set of coefficients forms the staterepresentation s_(t). Therefore, if the mapping function ƒ(s_(t)) andthe state representation s_(t) for a time point t are known, thefunctional representation f(.) can be determined, and thus the value ofthe physiologic signal can be calculated according to the determinedfunctional representation f(.). Further, at different time points, thefunctional representation f(.) used to calculate the physiologic signalx(t) may be different. For example, for the same physiologic signal, thefunctional representation f(.) may be a quadratic function for one statebut a cubic function for another state.

For example, assume the physiologic signal is a univariate signal andcan be represented as x(t), and the functional representation f(.) attime t is a cubic polynomial defined by four parameters a₁(t), a₂(t),a₃(t), and a₄(t), which form the state representation s_(t)=[a₁(t),a₂(t), a₃(t), a₄(t)]^(T). Hence the physiologic signal x(t) in thisexample can be calculated by: x(t)=a₁(t)+a₂(t)t+a₃(t)t²+a₄(t)t³. Ingeneral, when the physiologic signal is a multivariate signal, each ofa₁(t), a₂(t), a₃(t), and a₄(t) can also be multivariate.

According to the present disclosure, once the functional representationf(.) is determined according to EQN. 8, the predicted future value ofthe physiologic signal at a future time t+δ, i.e., x(t+δ), can becalculated using EQN. 7 and historical measured values of thephysiologic signal. The set of historical measured values of thephysiologic signal includes the measured values for a time rangestarting from a time in the past, referred to herein as time t₀, to acurrent time t_(c). This time range is also referred to herein as a“measurement time range.” The process for calculating the future valueof the physiologic signal using EQN. 7, EQN. 8, and the historicalmeasured values is described below.

For each of one or more of the time points in the measurement timerange, the state representation s_(t) is estimated based on the set ofhistorical measured values. For example, the state representation s_(t)for a particular time point t₁ can be determined based on the measuredvalues of the physiologic signal at the time point t₁ and one or moreprevious time points. In some embodiments, the state representations_(t) is estimated for each of the time points in the measurement timerange. According to the present disclosure, the state representations_(t) can be estimated using various appropriate methods, such as aregression method. An estimated state representation is also referred toherein as a historical state representation, and the set of estimatedstate representations is also referred to herein as a staterepresentation history.

Based on the state representation history, a predicted staterepresentation ŝ_(t+δ) at the future time t+δ can be calculated using anappropriate prediction algorithm. In some embodiments, a regressionmethod, such as kernel density estimation, support vector regression, orlinear regression, can be used to calculate the predicted staterepresentation ŝ_(t+δ). In some embodiments, an adaptive filteringscheme, such as normalized linear mean-square (nLMS) filter, can be usedto calculate the predicted state representation ŝ_(t+δ).

Based on the predicted state representation ŝ_(t+δ), a predictedfunctional representation f(.) at the future time t+δ can be determinedby substituting the state representation ŝ_(t+δ) into EQN. 8. Further,the predicted future value {circumflex over (x)}(t+δ) can be calculatedby substituting the future time t+δ into EQN. 7 with the predictedfunctional representation, i.e., {circumflex over (x)}(t+δ)=ƒ(.)(t+δ).

Before the above-described predictor can be used to predict futurevalues of the physiologic signal, it may need to be trained to determinethe proper correlation between the functional representation ƒ(.) andthe state representation s_(t), i.e., to determine the proper mappingfunction ƒ(s_(t)). Consistent with the present disclosure, historicalmeasured values of the physiologic signal can be used to train thepredictor. The set of historical measured values of the physiologicsignal used for training the predictor can be the same as or differentfrom the set of historical measured values used to calculate thepredicted state representation ŝ_(t+δ). In the present disclosure, theset of historical measured values of the physiologic signal used fortraining the predictor is also referred to as a “training data set.”

With the given training data set, a state representation s_(t) for eachof the time points in the training data set can be estimated using, forexample, a regression method, such as kernel density estimation. Anestimated state representation s_(t) based on the training data set isalso referred to herein as a training state representation, and the setof training state representations is also referred to herein as atraining state representation set. As an example, the predictor can betrained to find the proper mapping function ƒ(s_(t)) using one or moretraining pairs, each of which may include the estimated staterepresentation at a particular time t′, i.e., s_(t), and the value ofphysiologic signal at time t′+δ, i.e., x(t′+δ). Various appropriatemethods can be used for the training purpose. For example, a regressionmethod, such as kernel density estimation, can be used for the trainingpurpose.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments in which theinvention can be practiced. These embodiments are also referred toherein as “examples.” Such examples can include elements in addition tothose shown or described. However, examples in which only those elementsshown or described are provided. Moreover, any combination orpermutation of those elements shown or described (or one or more aspectsthereof), either with respect to a particular example (or one or moreaspects thereof), or with respect to other examples (or one or moreaspects thereof) shown or described herein are within the scope of thepresent disclosure.

In the event of inconsistent usages between this document and anydocuments so incorporated by reference, the usage in this documentcontrols.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In this document, the terms “including” and “inwhich” are used as the plain-English equivalents of the respective terms“comprising” and “wherein.” Also, in the following claims, the terms“including” and “comprising” are open-ended, that is, a system, device,article, composition, formulation, or process that includes elements inaddition to those listed after such a term in a claim are still deemedto fall within the scope of that claim. Moreover, in the followingclaims, the terms “first,” “second,” and “third,” etc. are used merelyas labels, and are not intended to impose numerical requirements ontheir objects.

Method examples described herein can be machine or computer-implementedat least in part. Some examples can include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods can include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code can include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, in an example, the code can be tangiblystored on one or more volatile, non-transitory, or non-volatile tangiblecomputer-readable media, such as during execution or at other times.Examples of these tangible computer-readable media can include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments can be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is provided to complywith 37 C.F.R. § 1.72(b), to allow the reader to quickly ascertain thenature of the technical disclosure. It is submitted with theunderstanding that it will not be used to interpret or limit the scopeor meaning of the claims. Also, in the above Detailed Description,various features may be grouped together to streamline the disclosure.This should not be interpreted as intending that an unclaimed disclosedfeature is essential to any claim. Rather, inventive subject matter maylie in less than all features of a particular disclosed embodiment.Thus, the following claims are hereby incorporated into the DetailedDescription as examples or embodiments, with each claim standing on itsown as a separate embodiment, and it is contemplated that suchembodiments can be combined with each other in various combinations orpermutations. The scope of the invention should be determined withreference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. An image-guided therapy delivery system,comprising: a therapy generator configured to generate a therapy beamdirected to a time-varying therapy locus, within a therapy recipient,the therapy generator comprising a therapy output to direct the therapybeam according to a therapy protocol; an imaging input configured toreceive imaging information about a time-varying target locus within thetherapy recipient; and a therapy controller configured to: determine alatency associated with the therapy generator; automatically generate apredicted target locus by applying an earlier target locus extractedfrom the imaging information and the determined latency associated withthe therapy generator to a cyclic motion model associated with thetherapy recipient; and automatically generate an updated therapyprotocol to align the time-varying therapy locus with the predictedtarget locus.
 2. The image-guided therapy delivery system of claim 1,wherein the therapy controller is configured to automatically generatethe predicted target locus by: extracting information indicative of afeature from the imaging information, the feature corresponding to theearlier target locus; determining a phase of the cyclic motion modelcorresponding to a location of the feature; estimating a change in thelocation of the feature using a later phase of the cyclic motion modelcorresponding to a scheduled time of an upcoming therapy delivery andthe determined latency; and applying information indicative of theestimated change in the location of the feature to provide the predictedtarget locus.
 3. The image-guided therapy delivery system of claim 2,wherein the therapy controller is configured to automatically generatethe predicted target locus by: identifying a boundary of the earliertarget locus and determining a spatial point of the earlier target locusaccording to the identified boundary; and relocating the determinedspatial point of the earlier target locus using information indicativeof the change in the location of the feature.
 4. The image-guidedtherapy delivery system of claim 3, wherein identifying the boundary ofthe earlier target locus includes using information about a differencein intensity between adjacent pixels or voxels.
 5. The image-guidedtherapy delivery system of claim 1, wherein the cyclic motion modelspecifies a motion of the target locus in at least one spatial dimensionwith respect to time or phase.
 6. The image-guided therapy deliverysystem of claim 5, wherein: the cyclic motion model is established atleast in part using a series of two or more acquisitions ofthree-dimensional imaging information of a region; and the imaging inputis configured to receive at least one-dimensional imaging informationabout the time-varying target locus within the therapy recipient afterestablishing the cyclic motion model.
 7. The image-guided therapydelivery system of claim 1, wherein the therapy controller is configuredto control one or more actuators coupled to a moveable platformconfigured to support the therapy recipient, wherein the moveableplatform is moved according to the time-varying locus.
 8. Theimage-guided therapy delivery system of claim 1, wherein the therapycontroller is configured to control one or more apertures of amulti-leaf collimator, the apertures configured to shape the therapybeam based on the time-varying locus.
 9. The image-guided therapydelivery system of claim 1, wherein the therapy controller is configuredto control one or more actuators coupled to the therapy output, theactuators configured to position the therapy output to establish aspecified therapy beam direction.
 10. The image-guided therapy deliverysystem of claim 1, wherein the latency is a time period between anearlier image acquisition at a first time and a scheduled upcomingtherapy delivery at a second time.
 11. A method of adapting a therapyprotocol in response to a time-varying target locus in an image-guidedtherapy delivery system, the method comprising: receiving imaginginformation about the time-varying target locus within a therapyrecipient; determining a latency associated the a therapy generator ofthe therapy delivery system; automatically generating a predicted targetlocus by applying an earlier target locus extracted from the imaginginformation and the determined latency associated with the therapygenerator to a cyclic motion model associated with the therapyrecipient; and automatically generating an updated therapy protocol toalign the therapy locus with the predicted target locus, the therapylocus established by a therapy beam provided by the therapy generator.12. The method of claim 11, wherein automatically generating thepredicted target locus comprises: extracting information indicative of afeature from the imaging information, the feature corresponding to theearlier target locus; determining a phase of the cyclic motion modelcorresponding to location of the feature; estimating a change in thelocation of the feature using a later phase of the cyclic motion modelcorresponding to a scheduled time of upcoming therapy delivery and thedetermined latency; and applying information indicative of the estimatedchange in the location of the feature to provide the predicted targetlocus.
 13. The method of claim 12, wherein automatically generating thepredicted target locus comprises: identifying a boundary of the earliertarget locus and determining a spatial point of the earlier target locusaccording to the identified boundary; and relocating the determinedspatial point of the earlier target locus using information indicativeof the change in the location of the feature.
 14. The method of claim13, wherein identifying the boundary of the earlier target locusincludes using information about a difference in intensity betweenadjacent pixels or voxels.
 15. The method of claim 12, wherein thecyclic motion model specifies a motion of the target locus in at leastone spatial dimension with respect to time or phase.
 16. The method ofclaim 12, comprising: establishing the cyclic motion model at least inpart using a series of two or more acquisitions of three-dimensionalimaging information of a region; and after establishing the cyclicmotion model, receiving at least one-dimensional imaging informationabout the time-varying target locus.
 17. The method of claim 11, whereinthe latency is a time period between an earlier image acquisition at afirst time and a scheduled upcoming therapy delivery at a second time.18. A medical system, comprising: a medical device including a controlprotocol, the control protocol being configured to control the medicaldevice; and a controller configured to: receive a measured data setcontaining values of a physiologic signal caused by an anatomicalstructure on a patient measured at a plurality of past time points;estimate a state representation for each of the past time pointsaccording to the measured data set, the state representation reflectingan internal state of the anatomical structure; determine a latencyassociated with the medical device; predict a future staterepresentation for a future time point based on the estimated staterepresentations and the determined latency associated with the medicaldevice; train a mapping function using historical measured values of thephysiologic signal; predict a functional representation for the futuretime point based on the future state representation and the mappingfunction mapping state representations to functional representations ofthe physiologic signal; calculate a future value of the physiologicsignal according to the predicted functional representation, and updatethe control protocol according to the future value.
 19. The medicalsystem of claim 18, wherein the internal state of the anatomicalstructure comprises a source of quasiperiodic motion.
 20. A method forupdating a control protocol of a medical device, comprising: receiving ameasured data set containing values of a physiologic signal caused by ananatomical structure on a patient measured at a plurality of past timepoints; estimating a state representation for each of the past timepoints according to the measured data set, the state representationreflecting an internal state of the anatomical structure; determining alatency associated with the medical device; predicting a future staterepresentation for a future time point based on the estimated staterepresentations and the determined latency associated with the medicaldevice; training a mapping function using historical measured values ofthe physiologic signal; predicting a functional representation for thefuture time point based on the future state representation and themapping function that maps state representations to functionalrepresentations; calculating a future value of the physiologic signalaccording to the predicted functional representation; and updating thecontrol protocol of the medical device according to the predicted futurevalue, the control protocol controlling the medical device.